Abstract
3D visual grounding is the task of localizing the object in a 3D scene which is referred by a description in natural language. With a wide range of applications ranging from autonomous indoor robotics to AR/VR, the task has recently risen in popularity. A common formulation to tackle 3D visual grounding is grounding-by-detection, where localization is done via bounding boxes. However, for real-life applications that require physical interactions, a bounding box insufficiently describes the geometry of an object. We therefore tackle the problem of dense 3D visual grounding, i.e. referral-based 3D instance segmentation. We propose a dense 3D grounding network ConcreteNet, featuring four novel stand-alone modules that aim to improve grounding performance for challenging repetitive instances, i.e. instances with distractors of the same semantic class. First, we introduce a bottom-up attentive fusion module that aims to disambiguate inter-instance relational cues, next, we construct a contrastive training scheme to induce separation in the latent space, we then resolve view-dependent utterances via a learned global camera token, and finally we employ multi-view ensembling to improve referred mask quality. ConcreteNet ranks \(1^{st}\) on the challenging ScanRefer online benchmark and has won the ICCV \(3^{rd}\) Workshop on Language for 3D Scenes “3D Object Localization” challenge. Our code is available at ouenal.github.io/concretenet/.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Classifying each point yields a more robust solution compared to the localization of 8 corner points in complete free 3D space.
- 2.
We believe that input camera positions are a reasonable assumption in indoor robotic applications and hope that this performance potential will motivate future research.
References
Achlioptas, P., Abdelreheem, A., Xia, F., Elhoseiny, M., Guibas, L.: ReferIt3D: neural listeners for fine-grained 3D object identification in real-world scenes. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 422–440. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_25
Cai, D., Zhao, L., Zhang, J., Sheng, L., Xu, D.: 3DJCG: a unified framework for joint dense captioning and visual grounding on 3D point clouds. In: CVPR (2022)
Chen, D.Z., Chang, A.X., Nießner, M.: ScanRefer: 3D object localization in RGB-D scans using natural language. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 202–221. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58565-5_13
Chen, D.Z., Wu, Q., Nießner, M., Chang, A.X.: D3Net: a unified speaker-listener architecture for 3D dense captioning and visual grounding. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXXII, pp. 487–505. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_29
Chen, J., Luo, W., Wei, X., Ma, L., Zhang, W.: HAM: hierarchical attention model with high performance for 3D visual grounding. arXiv preprint arXiv:2210.12513 (2022)
Chen, Z., Gholami, A., Nießner, M., Chang, A.X.: Scan2cap: context-aware dense captioning in RGB-d scans. In: CVPR (2021)
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR (2022)
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: ICCV (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Feng, M., et al.: Free-form description guided 3D visual graph network for object grounding in point cloud. In: ICCV (2021)
Goyal, A., Yang, K., Yang, D., Deng, J.: Rel3D: a minimally contrastive benchmark for grounding spatial relations in 3D. In: NIPS (2020)
Guo, Z., et al.: Viewrefer: grasp the multi-view knowledge for 3d visual grounding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15372–15383 (2023)
He, T., Shen, C., Van Den Hengel, A.: Dyco3d: robust instance segmentation of 3d point clouds through dynamic convolution. In: CVPR (2021)
Huang, P.H., Lee, H.H., Chen, H.T., Liu, T.L.: Text-guided graph neural networks for referring 3D instance segmentation. In: AAAI (2021)
Huang, S., Chen, Y., Jia, J., Wang, L.: Multi-view transformer for 3D visual grounding. In: CVPR (2022)
Jain, A., Gkanatsios, N., Mediratta, I., Fragkiadaki, K.: Bottom up top down detection transformers for language grounding in images and point clouds. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXXVI, pp. 417–433. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20059-5_24
Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C.W., Jia, J.: Pointgroup: dual-set point grouping for 3d instance segmentation. In: CVPR (2020)
Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: Referitgame: referring to objects in photographs of natural scenes. In: EMNLP (2014)
Kong, C., Lin, D., Bansal, M., Urtasun, R., Fidler, S.: What are you talking about? text-to-image coreference. In: ICCV (2014)
Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Luo, J., et al.:: 3D-SPS: Single-stage 3D visual grounding via referred point progressive selection. In: CVPR (2022)
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A.L., Murphy, K.: Generation and comprehension of unambiguous object descriptions. In: ICCV (2016)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)
Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: ICCV (2015)
Prabhudesai, M., Tung, H.Y.F., Javed, S.A., Sieb, M., Harley, A.W., Fragkiadaki, K.: Embodied language grounding with 3D visual feature representations. In: CVPR (2020)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)
Roh, J., Desingh, K., Farhadi, A., Fox, D.: LanguageRefer: spatial-language model for 3D visual grounding. In: CoRL (2022)
Rozenberszki, D., Litany, O., Dai, A.: Language-grounded indoor 3D semantic segmentation in the wild. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXXIII, pp. 125–141. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19827-4_8
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54
Song, K., Tan, X., Qin, T., Lu, J., Liu, T.Y.: Mpnet: masked and permuted pre-training for language understanding. In: NIPS (2020)
Vu, T., Kim, K., Luu, T.M., Nguyen, X.T., Yoo, C.D.: Softgroup for 3d instance segmentation on 3d point clouds. In: CVPR (2022)
Wu, Y., Cheng, X., Zhang, R., Cheng, Z., Zhang, J.: Eda: explicit text-decoupling and dense alignment for 3d visual grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19231–19242 (2023)
Wu, Y., Shi, M., Du, S., Lu, H., Cao, Z., Zhong, W.: 3D instances as 1D kernels. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXIX, pp. 235–252. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19818-2_14
Yang, Z., Zhang, S., Wang, L., Luo, J.: SAT: 2D semantics assisted training for 3D visual grounding. In: ICCV (2021)
Yuan, Z., et al.: InstanceRefer: cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring. In: ICCV (2021)
Zhang, P., et al.: Multi-scale vision Longformer: a new vision transformer for high-resolution image encoding. In: ICCV (2021)
Zhang, Y., Gong, Z., Chang, A.X.: Multi3drefer: grounding text description to multiple 3d objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15225–15236 (2023)
Zhao, L., Cai, D., Sheng, L., Xu, D.: 3DVG-transformer: relation modeling for visual grounding on point clouds. In: ICCV (2021)
Acknowledgments
This work is funded by Toyota Motor Europe via the research project TRACE-Zürich.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Unal, O., Sakaridis, C., Saha, S., Van Gool, L. (2025). Four Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15134. Springer, Cham. https://doi.org/10.1007/978-3-031-73116-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-73116-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-73115-0
Online ISBN: 978-3-031-73116-7
eBook Packages: Computer ScienceComputer Science (R0)